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Deep learning-based automatic planning with risk minimization for brain tumor biopsy

Year 2025, Volume: 40 Issue: 1, 487 - 500, 16.08.2024
https://doi.org/10.17341/gazimmfd.1348325

Abstract

Biopsy emerges as a critical procedure for determining tumor types and establishing pathological diagnoses. This process encompasses two primary stages: planning and surgical intervention. During the planning stage, anatomical points in the patient's brain are marked based on MRI data, known to take an average of four hours. However, the accuracy deficiencies, subjective variations, and time consumption associated with manual marking reveal the critical need for an automated planning tool. In this study, we propose a biopsy planning method, entirely automated and incorporating cutting-edge deep learning architectures, on MRI and MRA data. The suggested approach aims to execute biopsy planning rapidly, consistently, and repeatably. The method consists of four main stages: 1) Removal of the brain's upper shell, 2) Tumor detection and target point determination, 3) Segmentation of the brain's vascular network, and 4) Combination of the three stages and risk calculation for optimal trajectory determination. This automatic method has been validated with 42 patient data in ITKTubeTK. Furthermore, this study, prepared as a "3D Slicer" plugin, is offered as a free computer-assisted tool for clinics. In subsequent phases of the research, integration of fMRI data is planned to further enhance risk calculation.

Project Number

122E495

References

  • 1. Herrera E., Stereotactic neurosurgery in children and adolescents, Child’s Nervous System, 15, 256–260, 1999.
  • 2. Mishra S., Hologram the future of medicine – from star wars to clinical imaging, Indian Heart Journal, 69, 566 – 567, 2017.
  • 3. Dlaka D., Chudy D., Jerbić B., Kaštelančić A., Raguž M., Robot-assisted stereotactic and spinal neurosurgery: A review of literature, 2021 44th International Convention on Information, Communication and Electronic Technology, Opatija-Croatia 1185–1190, 15 November 2021.
  • 4. Marcus H.J., Vakharia V. N., Ourselin S., Duncan J., Tisdall M., Aquilina K., Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis, Child’s Nervous System, 34, 1299–1309, 2018.
  • 5. Zimmer Biomet, ROSA, https://www.zimmerbiomet.com/en/patients-caregivers/rosa-robotic-technology.html, Erişim tarihi Temmuz 30, 2023.
  • 6. Amin D. V., Lunsford L. D., Volumetric Resection Using the SurgiScope®: A Quantitative Accuracy Analysis of Robot-Assisted Resection, Stereotactic and Functional Neurosurgery, 82, 250–253, 2005.
  • 7. Renishaw, Neuromate, https://www.renishaw.com.tr/tr/neuromate-stereotactic-robot--10712, Yayın tarihi 2001, Erişim tarihi Ağustos 3, 2023.
  • 8. Shamir R., Freiman M., Joskowicz L., Shoham M., Zehavi E., Shoshan Y., Robot-assisted image-guided targeting for minimally invasive neurosurgery: Planning, registration, and in-vitro experiment, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005, Heidelberg-Berlin, 131–138, 2005.
  • 9. Serletis D., Pait T. G., Early craniometric tools as a predecessor to neurosurgical stereotaxis, Journal of Neurosurgery, 124 (6), 1867–1874, 2016.
  • 10. Fomenko A., Serletis D., Robotic stereotaxy in cranial neurosurgery: a qualitative systematic review, Neurosurgery, 83(4), 642–650, 2018.
  • 11. Trope M., Shamir R. R., Joskowicz L., Medress Z., Rosenthal G., Mayer A., Levin N., Bick A., Shoshan Y., The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery, International journal of computer assisted radiology and surgery, 10, 1127–1140, 2015.
  • 12. Bulut C., Ballı T., Yetkin F.E., Comparative classification performances of filter model feature selection algorithms in EEG based brain computer interface system, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2397-2408, 2023.
  • 13. Faria C., Erlhagen W., Rito M., De Momi E., Ferrigno G., Bicho E., Review of robotic technology for stereotactic neurosurgery, IEEE Rev. Biomed. Eng., 8, 125–137, 2015.
  • 14. Renier C., Targeting inaccuracy caused by mechanical distortion of the leksell stereotactic frame during fixation, J. Appl. Clin. Med. Phys., 20, 27 – 36, 2019.
  • 15. Lim D. H., Kim S. Y., Na Y. C., Cho J. M., Navigation guided biopsy is as effective as frame-based stereotactic biopsy, Journal of Personalized Medicine, 13, 5, 2023.
  • 16. Hamzé N., Bilger A., Duriez C., Cotin S., Essert C., Anticipation of brain shift in Deep Brain Stimulation automatic planning, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan-Italy, 3635-3638, August 25-26, 2015.
  • 17. Das S., Stereotactic biopsy in the diagnosis of small brain lesion, Journal of Bangladesh College of Physicians and Surgeons, 39, 24–35, 2020.
  • 18. Dundar T. T., Yurtsever I., Pehlivanoglu M. K., Yildiz U., Eker A., Demir M. A., Mutluer A. S., Tektas R., Kazan M. S., Kitis S., Gokoglu A., Dogan I., Duru N., Machine learning-based surgical planning for neurosurgery: Artificial intelligent approaches to the cranium, Frontiers in Surgery, 9, 2022.
  • 19. Yavas G., Caliskan K. E., Cagli M. S., Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique, Neurosurgical Focus, 51 (2), E20, 2021.
  • 20. Hu Y., Cai P., Zhang H., Adilijiang A., Peng J., Li Y., Che S., Lan F., Liu C., A comparation between frame-based and robot-assisted in stereotactic biopsy, Frontiers in Neurology, 13, 928070, 2022.
  • 21. Marcus H. J., Vakharia V. N., Sparks R., Rodionov R., Kitchen N., McEvoy A. W., Miserocchi A., Thorne L., Ourselin S., Duncan J. S., Computer-assisted versus manual planning for stereotactic brain biopsy: a retrospective comparative pilot study, Operative Neurosurgery, 18 (4), 417, 2020.
  • 22. Zanello M., Carron R., Peeters S., Gori P., Roux A., Bloch I., Oppenheim C., Pallud J., Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives. Neurosurg Rev, 44, 867–888, 2021.
  • 23. CASILab at the University of North Carolina-C. Itktubetk-bullitt-healthy mr database. Kitware Data. https://data.kitware.com/#collection/591086ee8d777f16d01e0724/folder/58a372fa8d777f0721a64dfb. Erişim tarihi Ağustos 3, 2023.
  • 24. Isensee F., Schell M., Pflueger I., Brugnara G., Bonekamp D., Neuberger U., Wick A., Schlemmer H. P., Heiland S., Wick W., Bendszus M., Maier-Hein K. H., Kickingereder P., Automated brain extraction of multisequence mri using artificial neural networks, Human Brain Mapping, 40 (17), 4952–4964, 2019.
  • 25. Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.-C., Pujol S., Bauer C., Jennings D., Fennessy F., Sonka M., Buatti J., Aylward S., Miller J. V., Pieper S., Kikinis R., 3d slicer as an image computing platform for the quantitative imaging network, Magnetic Resonance Imaging, 30 (9), 1323–1341, 2012.
  • 26. The Trustees of the University of Pennsylvania. The brain tumor segmentation (brats) challenges. https://www.med.upenn.edu/cbica/brats/. Erişim tarihi Şubat 13, 2022.
  • 27. Hatamizadeh A., Nath V., Tang Y., Yang D., Roth H., Xu D., Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images, 2022.
  • 28. Hatamizadeh A., Tang Y., Nath V., Yang D., Myronenko A., Landman B., Roth H. R., Xu D., Unetr: Transformers for 3d medical image segmentation, Proceedings of the IEEE/CVF winter conference on applications of computer vision, 574–584, 2022.
  • 29. Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich-Germany, 18, 234–241, October 5-9, 2015.
  • 30. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., Polosukhin I., Attention is all you need, Advances in neural information processing systems, 30, 2017.
  • 31. Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., et al., An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 32. “vtkobbtree class reference.” https://vtk.org/doc/nightly/html/classvtkOBBTree.html. Erişim tarihi Ocak 13, 2023.
  • 33. Sorensen, T., A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske skrifter, 5, 1-34, 1948.
  • 34. Willmott, C. J., & Matsuura, K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30 (1), 79-82, 2005.
  • 35. Federer, H., Curvature measures. Transactions of the American Mathematical Society, 93 (3), 418-491, 1959.
  • 36. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P, Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13 (4), 600-612, 2004.

Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama

Year 2025, Volume: 40 Issue: 1, 487 - 500, 16.08.2024
https://doi.org/10.17341/gazimmfd.1348325

Abstract

Biyopsi, tümör türünün belirlenmesi ve patolojik teşhisin konulması için kritik bir işlem olarak karşımıza çıkar. Bu süreç, özellikle tümörlü yapıdan parça alınarak gerçekleştirilen planlama ve cerrahi müdahale olmak üzere iki temel aşamayı içerir. Planlama aşamasında, MRI verisi üzerinden hastanın beynindeki anatomik noktaların işaretlemesi yapılır ve bu sürecin ortalama dört saat sürdüğü bilinmektedir. Ancak, manuel işaretlemeyle yapılan bu tür planlamaların doğruluk eksiklikleri, sübjektif varyasyonlar ve zaman alıcılığı, otomatik bir planlama aracının kritik bir ihtiyaç olduğunu göstermektedir. Bu çalışmada, MRI ve MRA verisi üzerinden tam otomatik, son teknoloji derin öğrenme mimarilerini içeren bir biyopsi planlama yöntemi önerilmektedir. Önerilen bu yöntem, biyopsi planlamasını hızlı, tutarlı ve tekrarlanabilir bir şekilde gerçekleştirmeyi amaçlamaktadır. Yöntem dört ana aşamadan oluşmaktadır: 1-) Beyinin üst kabuk bölgesinin çıkarılması, 2-) Tümör tespiti ve hedef noktasının belirlenmesi, 3-) Beyin damar ağacının bölütlenmesi, 4-) Optimum yörünge tespiti için üç aşamanın kombinasyonu ve risk hesaplanması. Bu otomatik yöntem, ITKTubeTK'deki 42 hasta verisiyle doğrulanmıştır. Ayrıca, "3D Slicer" eklentisi olarak hazırlanan bu çalışma, klinikler için ücretsiz bir bilgisayar destekli araç olarak sunulmaktadır. Araştırmanın ilerleyen aşamalarında, risk hesaplamasını daha da geliştirmek amacıyla fMRI verisinin entegrasyonu üzerine çalışılması planlanmaktadır.

Supporting Institution

TÜBİTAK ARDEB

Project Number

122E495

Thanks

Araştırmamıza 122E495 proje numarası ile cömertçe destek olan TÜBİTAK ARDEB'e içtenlikle teşekkürlerimizi sunarız. Sağladıkları fon, çalışmamızın gerçekleşmesinde kritik bir rol oynamıştır ve bu nedenle kendilerine derin bir minnettarlık duymaktayız.

References

  • 1. Herrera E., Stereotactic neurosurgery in children and adolescents, Child’s Nervous System, 15, 256–260, 1999.
  • 2. Mishra S., Hologram the future of medicine – from star wars to clinical imaging, Indian Heart Journal, 69, 566 – 567, 2017.
  • 3. Dlaka D., Chudy D., Jerbić B., Kaštelančić A., Raguž M., Robot-assisted stereotactic and spinal neurosurgery: A review of literature, 2021 44th International Convention on Information, Communication and Electronic Technology, Opatija-Croatia 1185–1190, 15 November 2021.
  • 4. Marcus H.J., Vakharia V. N., Ourselin S., Duncan J., Tisdall M., Aquilina K., Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis, Child’s Nervous System, 34, 1299–1309, 2018.
  • 5. Zimmer Biomet, ROSA, https://www.zimmerbiomet.com/en/patients-caregivers/rosa-robotic-technology.html, Erişim tarihi Temmuz 30, 2023.
  • 6. Amin D. V., Lunsford L. D., Volumetric Resection Using the SurgiScope®: A Quantitative Accuracy Analysis of Robot-Assisted Resection, Stereotactic and Functional Neurosurgery, 82, 250–253, 2005.
  • 7. Renishaw, Neuromate, https://www.renishaw.com.tr/tr/neuromate-stereotactic-robot--10712, Yayın tarihi 2001, Erişim tarihi Ağustos 3, 2023.
  • 8. Shamir R., Freiman M., Joskowicz L., Shoham M., Zehavi E., Shoshan Y., Robot-assisted image-guided targeting for minimally invasive neurosurgery: Planning, registration, and in-vitro experiment, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005, Heidelberg-Berlin, 131–138, 2005.
  • 9. Serletis D., Pait T. G., Early craniometric tools as a predecessor to neurosurgical stereotaxis, Journal of Neurosurgery, 124 (6), 1867–1874, 2016.
  • 10. Fomenko A., Serletis D., Robotic stereotaxy in cranial neurosurgery: a qualitative systematic review, Neurosurgery, 83(4), 642–650, 2018.
  • 11. Trope M., Shamir R. R., Joskowicz L., Medress Z., Rosenthal G., Mayer A., Levin N., Bick A., Shoshan Y., The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery, International journal of computer assisted radiology and surgery, 10, 1127–1140, 2015.
  • 12. Bulut C., Ballı T., Yetkin F.E., Comparative classification performances of filter model feature selection algorithms in EEG based brain computer interface system, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2397-2408, 2023.
  • 13. Faria C., Erlhagen W., Rito M., De Momi E., Ferrigno G., Bicho E., Review of robotic technology for stereotactic neurosurgery, IEEE Rev. Biomed. Eng., 8, 125–137, 2015.
  • 14. Renier C., Targeting inaccuracy caused by mechanical distortion of the leksell stereotactic frame during fixation, J. Appl. Clin. Med. Phys., 20, 27 – 36, 2019.
  • 15. Lim D. H., Kim S. Y., Na Y. C., Cho J. M., Navigation guided biopsy is as effective as frame-based stereotactic biopsy, Journal of Personalized Medicine, 13, 5, 2023.
  • 16. Hamzé N., Bilger A., Duriez C., Cotin S., Essert C., Anticipation of brain shift in Deep Brain Stimulation automatic planning, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan-Italy, 3635-3638, August 25-26, 2015.
  • 17. Das S., Stereotactic biopsy in the diagnosis of small brain lesion, Journal of Bangladesh College of Physicians and Surgeons, 39, 24–35, 2020.
  • 18. Dundar T. T., Yurtsever I., Pehlivanoglu M. K., Yildiz U., Eker A., Demir M. A., Mutluer A. S., Tektas R., Kazan M. S., Kitis S., Gokoglu A., Dogan I., Duru N., Machine learning-based surgical planning for neurosurgery: Artificial intelligent approaches to the cranium, Frontiers in Surgery, 9, 2022.
  • 19. Yavas G., Caliskan K. E., Cagli M. S., Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique, Neurosurgical Focus, 51 (2), E20, 2021.
  • 20. Hu Y., Cai P., Zhang H., Adilijiang A., Peng J., Li Y., Che S., Lan F., Liu C., A comparation between frame-based and robot-assisted in stereotactic biopsy, Frontiers in Neurology, 13, 928070, 2022.
  • 21. Marcus H. J., Vakharia V. N., Sparks R., Rodionov R., Kitchen N., McEvoy A. W., Miserocchi A., Thorne L., Ourselin S., Duncan J. S., Computer-assisted versus manual planning for stereotactic brain biopsy: a retrospective comparative pilot study, Operative Neurosurgery, 18 (4), 417, 2020.
  • 22. Zanello M., Carron R., Peeters S., Gori P., Roux A., Bloch I., Oppenheim C., Pallud J., Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives. Neurosurg Rev, 44, 867–888, 2021.
  • 23. CASILab at the University of North Carolina-C. Itktubetk-bullitt-healthy mr database. Kitware Data. https://data.kitware.com/#collection/591086ee8d777f16d01e0724/folder/58a372fa8d777f0721a64dfb. Erişim tarihi Ağustos 3, 2023.
  • 24. Isensee F., Schell M., Pflueger I., Brugnara G., Bonekamp D., Neuberger U., Wick A., Schlemmer H. P., Heiland S., Wick W., Bendszus M., Maier-Hein K. H., Kickingereder P., Automated brain extraction of multisequence mri using artificial neural networks, Human Brain Mapping, 40 (17), 4952–4964, 2019.
  • 25. Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.-C., Pujol S., Bauer C., Jennings D., Fennessy F., Sonka M., Buatti J., Aylward S., Miller J. V., Pieper S., Kikinis R., 3d slicer as an image computing platform for the quantitative imaging network, Magnetic Resonance Imaging, 30 (9), 1323–1341, 2012.
  • 26. The Trustees of the University of Pennsylvania. The brain tumor segmentation (brats) challenges. https://www.med.upenn.edu/cbica/brats/. Erişim tarihi Şubat 13, 2022.
  • 27. Hatamizadeh A., Nath V., Tang Y., Yang D., Roth H., Xu D., Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images, 2022.
  • 28. Hatamizadeh A., Tang Y., Nath V., Yang D., Myronenko A., Landman B., Roth H. R., Xu D., Unetr: Transformers for 3d medical image segmentation, Proceedings of the IEEE/CVF winter conference on applications of computer vision, 574–584, 2022.
  • 29. Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich-Germany, 18, 234–241, October 5-9, 2015.
  • 30. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., Polosukhin I., Attention is all you need, Advances in neural information processing systems, 30, 2017.
  • 31. Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., et al., An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 32. “vtkobbtree class reference.” https://vtk.org/doc/nightly/html/classvtkOBBTree.html. Erişim tarihi Ocak 13, 2023.
  • 33. Sorensen, T., A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske skrifter, 5, 1-34, 1948.
  • 34. Willmott, C. J., & Matsuura, K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30 (1), 79-82, 2005.
  • 35. Federer, H., Curvature measures. Transactions of the American Mathematical Society, 93 (3), 418-491, 1959.
  • 36. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P, Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13 (4), 600-612, 2004.
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Computer Vision, Image Processing, Deep Learning, Artificial Intelligence (Other), Biomedical Imaging, Biomedical Diagnosis
Journal Section Makaleler
Authors

Mustafa Şahin 0009-0006-1701-4566

Emrullah Şahin 0000-0002-3390-6285

Edanur Özdemir 0000-0002-0311-9838

Fatih Talu 0000-0003-1166-8404

Sait Öztürk 0000-0002-7655-0127

Project Number 122E495
Early Pub Date July 1, 2024
Publication Date August 16, 2024
Submission Date August 23, 2023
Acceptance Date March 23, 2024
Published in Issue Year 2025 Volume: 40 Issue: 1

Cite

APA Şahin, M., Şahin, E., Özdemir, E., Talu, F., et al. (2024). Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(1), 487-500. https://doi.org/10.17341/gazimmfd.1348325
AMA Şahin M, Şahin E, Özdemir E, Talu F, Öztürk S. Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama. GUMMFD. August 2024;40(1):487-500. doi:10.17341/gazimmfd.1348325
Chicago Şahin, Mustafa, Emrullah Şahin, Edanur Özdemir, Fatih Talu, and Sait Öztürk. “Beyin tümörü Biyopsisi için Derin öğrenme Tabanlı Risk Minimizasyonlu Otomatik Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, no. 1 (August 2024): 487-500. https://doi.org/10.17341/gazimmfd.1348325.
EndNote Şahin M, Şahin E, Özdemir E, Talu F, Öztürk S (August 1, 2024) Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 1 487–500.
IEEE M. Şahin, E. Şahin, E. Özdemir, F. Talu, and S. Öztürk, “Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama”, GUMMFD, vol. 40, no. 1, pp. 487–500, 2024, doi: 10.17341/gazimmfd.1348325.
ISNAD Şahin, Mustafa et al. “Beyin tümörü Biyopsisi için Derin öğrenme Tabanlı Risk Minimizasyonlu Otomatik Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/1 (August 2024), 487-500. https://doi.org/10.17341/gazimmfd.1348325.
JAMA Şahin M, Şahin E, Özdemir E, Talu F, Öztürk S. Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama. GUMMFD. 2024;40:487–500.
MLA Şahin, Mustafa et al. “Beyin tümörü Biyopsisi için Derin öğrenme Tabanlı Risk Minimizasyonlu Otomatik Planlama”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 40, no. 1, 2024, pp. 487-00, doi:10.17341/gazimmfd.1348325.
Vancouver Şahin M, Şahin E, Özdemir E, Talu F, Öztürk S. Beyin tümörü biyopsisi için derin öğrenme tabanlı risk minimizasyonlu otomatik planlama. GUMMFD. 2024;40(1):487-500.